Bandwidth limited object recognition in high resolution imagery
This addresses bandwidth limitations for object recognition in critical communication scenarios, representing an incremental improvement over compression-based approaches.
The paper tackles bandwidth optimization for object detection in high-resolution imagery by developing two active information seeking models that identify promising regions in low-resolution images and progressively request higher-resolution data. Results show the system saves up to one order of magnitude in bandwidth compared to JPEG compression with minimal recognition performance loss.
This paper proposes a novel method to optimize bandwidth usage for object detection in critical communication scenarios. We develop two operating models of active information seeking. The first model identifies promising regions in low resolution imagery and progressively requests higher resolution regions on which to perform recognition of higher semantic quality. The second model identifies promising regions in low resolution imagery while simultaneously predicting the approximate location of the object of higher semantic quality. From this general framework, we develop a car recognition system via identification of its license plate and evaluate the performance of both models on a car dataset that we introduce. Results are compared with traditional JPEG compression and demonstrate that our system saves up to one order of magnitude of bandwidth while sacrificing little in terms of recognition performance.